Alternate Title

NISMART-2

Summary

The National Incidence Studies of Missing, Abducted,
Runaway, and Thrownaway Children (NISMART) were undertaken in response
to the mandate of the 1984 Missing Children's Assistance Act
(Pub.L. 98-473) that requires the Office of Juvenile Justice and
Delinquency Prevention (OJJDP) to conduct periodic national incidence
studies to determine the actual number of children reported missing
and the number of missing children who are recovered for a given
year. The first such study, NISMART-1 (NATIONAL INCIDENCE STUDIES OF
MISSING, ABDUCTED, RUNAWAY, AND THROWNAWAY CHILDREN (NISMART), 1988
[ICPSR 9682]), was conducted from 1988 to 1989 and addressed this
mandate by defining major types of missing child episodes and
estimating the number of children who experienced missing child
episodes of each type in 1988. At that time, the lack of a
standardized definition of a "missing child" made it impossible to
provide a single estimate of missing children. As a result, one of the
primary goals of NISMART-2 was to develop a standardized definition
and provide unified estimates of the number of missing children in the
United States. Both NISMART-1 and NISMART-2 comprise several component
datasets designed to provide a comprehensive picture of the population
of children who experienced qualifying episodes, with each component
focusing on a different aspect of the missing child population. The
Household Survey -- Youth Data and the Household Survey -- Adult Data
(Parts 1-2) are similar but separate surveys, one administered to the
adult primary caretaker of the children in the sampled household and
the other to a randomly selected household youth aged 10 through 18 at
the time of interview. The Juvenile Facilities Data on Runaways (Part
3) sought to estimate the number of runaways from juvenile residential
facilities in order to supplement the household survey estimate of the
number of runaways from households. And the Law Enforcement Study
Data, by case perpetrator, and victim, (Parts 4-6) intended to
estimate the number of children who were victims of stereotypical
kidnappings and to obtain a sample of these cases for in-depth study.

Time Period(s)

Date of Collection

Sample

Parts 1-2: A list-assisted RDD (Random Digit Dial)
methodology was used to select a nationally representative sample of
telephone households from the GENESYS Sampling System frame.
Typically, a list-assisted RDD design is a one-stage random
selection process resulting in equal probabilities of selection
(EPSEM) design and no clustering, with each household treated as a
Primary Sampling Unit (PSU). A detailed description of the sampling
design is in HOUSEHOLD SURVEY METHODS TECHNICAL REPORT which is
included in this data collection. Part 3: A stratified, two-stage
sample design was applied. In the first stage, 30 Primary Sampling
Units (PSUs) were sampled from a listing of the national universe of
counties and groups of small, adjacent counties. The PSUs were
selected with probability proportionate to the number of children
(aged 0 to 17) according to the 1990 Census. Within the 30 PSUs, a
total of 922 eligible facilities were identified. This frame was
stratified by type of facility, and 75 facilities were sampled with
probability proportionate to size (PPS), using weighted capacity as
the measure of size. Parts 4-6: Nationally representative sample of
400 counties. Counties were selected with probabilities proportional
to size.

Time Method

Time Series

Universe

Parts 1-2: Households with at least one child aged 18 or
younger who had lived in the house for two consecutive weeks in the 12
months prior to screening. Part 3: All group homes, juvenile detention
centers, residential treatment centers, and runaway and homeless youth
shelters serving the selected counties. Parts 4-6: All law enforcement
agencies serving the selected counties.

Unit(s) of Observation

Parts 4-6: Victim, perpetrator,
and case

Parts 1-3: Youth

Data Type(s)

survey data

Mode of Data Collection

Response Rates

Part 1: 35 percent. Part 2: 61 percent. Part 3: The
JFS response rate for the institution-level interview was 100 percent.
A total of 74 facility-level interviews were completed from the 75
sampled facilities. Three of the sampled facilities were found to be
non-operational (hence ineligible), while a fourth sampled facility
actually consisted of three separate juvenile residential facilities,
all of which were included in the study. Thus, all in-scope facilities
participated in the facility-level interviews. The JFS response rate
for the episode-level interview was 93 percent. A total of 116
episode-level interviews were targeted. Episode-level interviews were
successfully completed for 108 of these targeted episodes, yielding a
93 percent completion rate. Parts 4-6: Phase 1 mail survey -- 91
percent, and Phase 2 followup telephone interview -- 99.3 percent.
Overall -- 90.6 percent.

Original Release Date

Version Date

2018-02-13

Version History

2018-02-13 2017-12-12 Due to confidentiality concerns, these data have been re-released as restricted use. A public use version may be available at a future date.

2007-07-19 ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection:

Weight

Parts 1-2: Survey weights were developed to compensate for
the higher probability of selection of multiple-line households for
children who lived in more than one household during the 12 months
prior to interview, and to adjust for nonresponse and undercoverage of
nontelephone households. The calculation of the RDD sample weights for
the Household Survey was done sequentially and consisted of four main
steps that included computing a base weight and various adjustments to
it. Details about the weighting procedures for the adult and youth
data are available in the NISMART-2 HOUSEHOLD SURVEY METHODOLOGY
TECHNICAL REPORT which is included in the data collection. Part 3: In
order to estimate the national prevalence of facility runaways with
known precision, it was necessary to compute facility weights, episode
weights, and replicate weights. The facility base weight was the
inverse of the unconditional probability of selecting the facility.
This was computed as the product of the PSU base weight and the
facility base weight. Because there was no nonresponse at the facility
level, the facility base weights were also the final facility-level
weights. Because data on individual runaways were obtained through the
episode-level interviews, the episode weights were equivalent to
youth-level weights. These were developed in two steps. First the
episode base weight was computed as the inverse of the conditional
probability of selecting an episode, taking into account both the
probability of selecting the facility that reported the episode and
the probability of selecting the episode in question from all episodes
reported by the facility. Second, because facilities could not provide
details on eight of the 116 episodes they had identified, nonresponse
adjustments were computed within facility categories to correct for
the absence of these interviews in the episode database. The fact that
the study used a multi-stage sampling design (sampling PSUs and then
facilities within PSUs) meant that special procedures were required to
produce appropriate standard errors for the study estimates. Most
standard statistical packages do not produce correct variances because
they treat the data as if all the elements in a sample had an equal
and independent chance of selection. To allow computation of
appropriate standard errors, the replication method was used to
develop replicate weights for both the facility and the episode-level
data. Thus, the final step in the weighting process was to develop
replicate weights. Parts 4-6: Three types of weights were computed for
sampled LES cases: agency weights, case weights, and replicate
weights. The agency weight is a function of the probability of having
selected the agency. Since all law enforcement agencies from the
sampled counties were included in the sample, this was the same as the
probability of having selected the county. The agency weights were
also adjusted for nonresponse and refusals, thus letting the
participating agencies represent the additional agencies that would
otherwise be unrepresented because of the agencies that did not
participate. Case weights took account of the probability of having
sampled the case, which was generally the same as the probability of
having sampled the agency from which the case originated. The case
weights were used to correct for the loss of the two case-level
refusals. The final step in the weighting process was the development
of replicate weights. The complex sampling design required special
procedures in order to compute the precision of the study estimates
and conduct statistical tests. The replication approach was used with
the LES data. The replication method required the development of
replicate weights. While the replication method is not the only way to
compute the precision of estimates from complex survey samples,
replication methods have been shown to have several advantages even
when other methods can be applied. The replicate samples are subsets
of the full sample created to mirror the design of the full
sample. The technique computes variance by measuring the variability
among replicates of the full sample. When a sample includes more than
20 percent of the population, the method of computing replicates must
be adjusted. As noted above, it was anticipated that the LES
methodology would yield a sample of stereotypical abductions that
would reflect a large percentage of the estimated total in the
nation. This indeed turned out to be the case. Because of this, the
replicates were adjusted with a "finite population factor" in order to
avoid overestimating variances Case sampling was used in a few cases
to reduce response burden in some large agencies in order to make it
feasible for the agency to participate in the study. In these
instances, case weights ensured that the sample cases represented all
cases in the agency.

Notes

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